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Inspired by natural evolution, evolutionary search algorithms have provenremarkably capable due to their dual abilities to radiantly explore throughdiverse populations and to converge to adaptive pressures. A large part of thisbehavior comes from the selection function of an evolutionary algorithm, whichis a metric for deciding which individuals survive to the next generation. Indeceptive or hard-to-search fitness landscapes, greedy selection often fails,thus it is critical that selection functions strike the correct balance betweengradient-exploiting adaptation and exploratory diversification. This paperintroduces Sel4Sel, or Selecting for Selection, an algorithm that searches forhigh-performing neural-network-based selection functions through ameta-evolutionary loop. Results on three distinct bitstring domains indicatethat Sel4Sel networks consistently match or exceed the performance of bothfitness-based selection and benchmarks explicitly designed to encouragediversity. Analysis of the strongest Sel4Sel networks reveals a generaltendency to favor highly novel individuals early on, with a gradual shifttowards fitness-based selection as deceptive local optima are bypassed.

Selecting for Selection: Learning To Balance Adaptive and Diversifying Pressures in Evolutionary Search